Performance and Persistence in Institutional Investment ...
Performance and Persistence in
Institutional Investment Management
JEFFREY A. BUSSE, AMIT GOYAL, and SUNIL WAHAL∗
Using new, survivorship bias-free data, we examine the performance and persistence in
performance of 4,617 active domestic equity institutional products managed by 1,448
investment management ﬁrms between 1991 and 2008. Controlling for the Fama-French
(1993) three factors and momentum, aggregate and average estimates of alphas are
statistically indistinguishable from zero. Even though there is considerable heterogeneity
in performance, there is only modest evidence of persistence in three-factor models and
little to none in four-factor models.
Jeﬀrey Busse is from the Goizueta Business School, Emory University, Amit Goyal is from the
Goizueta Business School, Emory University, and Sunil Wahal is from the W.P. Carey School of Busi-
ness, Arizona State University. We are indebted to Robert Stein and Margaret Tobiasen at Informa
Investment Solutions and to Jim Minnick and Frithjof van Zyp at eVestment Alliance for graciously pro-
viding data. We thank an anonymous referee, George Benston, Gjergji Cici, Kenneth French, William
Goetzmann (the European Finance Association discussant), Campbell Harvey (the Editor), Byoung-
Hyoun Hwang, Narasimhan Jegadeesh, and seminar participants at the 2006 European Finance As-
sociation meetings, 2008 Swiss Finance Association Meeting, Arizona State University, the College of
William and Mary, Emory University, Harvard University, HEC Lausanne, HEC Paris, Helsinki School
of Economics, National University of Singapore, Norwegian School of Economics and Business Adminis-
tration (NHH Bergen), Norwegian School of Management (BI Oslo), Singapore Management University,
UCLA, UNC-Chapel Hill, University of Georgia, University of Oregon, University of Virginia (Darden),
and VU University (Amsterdam) for helpful suggestions.
The twin questions of whether investment managers generate superior risk-adjusted
returns (“alpha”) and whether superior performance persists are central to our under-
standing of eﬃcient capital markets. Academic opinion on these issues revolves around
the most recent evidence incorporating either new data or improved measurement tech-
nology. Although Jensen’s (1968) original examination of mutual funds concludes that
funds do not have abnormal performance, later studies provide evidence that relative
performance persists over both short and long horizons.1 Carhart (1997), however, re-
ports that accounting for momentum in individual stock returns eliminates almost all
evidence of persistence among mutual funds (with one exception, the continued under-
performance of the worst performing funds (Berk and Xu (2004)). More recently, Bollen
and Busse (2005), Cohen, Coval, and P´stor (2005), Avramov and Wermers (2006), and
Kosowski et al. (2006) ﬁnd predictability in performance even after controlling for mo-
mentum. But Barras, Scaillet, and Wermers (2009) and Fama and French (2008) ﬁnd
little to no evidence of persistence or skill, particularly in the latter part of their sample
The attention given to the study of performance and persistence in retail mutual
funds is entirely warranted. The data are good, and this form of delegated asset man-
agement provides millions of investors access to ready-built portfolios. At the end of
2007, 7,222 equity, bond, and hybrid mutual funds were responsible for investing al-
most $9 trillion in assets (Investment Company Institute (2008)). However, an equally
large arm of delegated investment management receives much less attention, but is no
less important. At the end of 2006, more than 51,000 plan sponsors (public and pri-
vate retirement plans, endowments, foundations, and multi-employer unions) allocated
more than $7 trillion in assets to about 1,200 institutional asset managers (Standard
& Poor’s (2007)). In this paper, we examine the performance and persistence in per-
formance of portfolios managed by institutional investment management ﬁrms for these
Institutional asset management ﬁrms draw ﬁxed amounts of capital (referred to as
“mandates”) from plan sponsors. These mandates span a variety of asset classes, in-
cluding domestic equity, ﬁxed income, international equity, real estate securities, and
alternative assets (including hedge funds and private equity). Our focus is entirely on
domestic equity because, relative to other asset classes, it oﬀers the most widely accepted
benchmarks and risk-adjustment approaches. Within domestic equity, each mandate
calls for investment in a product that ﬁts a style identiﬁed by size and growth-value
gradations. Multiple mandates from diﬀerent plan sponsors can be managed together
in one portfolio or separately to reﬂect sponsor preferences and restrictions. However,
the essential elements of the portfolio strategy are identical and typically reﬂected in
the name of the composite product (e.g., large-cap value). This “product” (rather than
a derivative portfolio or fund) is our unit of observation.
Our data consist of composite returns and other information for 4,617 active domestic
equity institutional investment products oﬀered by 1,448 investment management ﬁrms
between 1991 and 2008. The data are free of survivorship bias, and all size and value-
growth gradations are represented. At the end of 2008, more than $2.5 trillion in assets
were invested in the products represented by these data.
We assess performance by estimating factor models cross-sectionally for each product
and by constructing equal- and value-weighted aggregate portfolios. Using the portfolio
approach, the equal-weighted three-factor alpha based on gross returns is an impressive
0.35% per quarter with a t-statistic of 2.52. However, value-weighting turns this alpha
into a statistically insigniﬁcant -0.01% per quarter. Correcting for momentum also
makes a big diﬀerence: the equal-weighted (value-weighted) four-factor alpha drops to
0.20% (increases to 0.05%) and is not statistically signiﬁcant. Fees further decimate
the returns to plan sponsors; the equal-weighted (value-weighted) net-of-fee four-factor
quarterly alpha is 0.01% (-0.10%) and again not statistically signiﬁcant.2
These aggregate results mask considerable cross-sectional variation in returns. The
diﬀerence between equal-weighted and value-weighted results indicates that, to the ex-
tent that it exists, superior performance is concentrated in smaller products. And,
the standard deviation of individual product alphas is high, at 0.78% per quarter for
four-factor alphas. To disentangle the issue of whether high (or low) realized alphas
are manifestations of skill or luck, we utilize the bootstrap approach of Kosowski et
al. (2006), as modiﬁed by Fama and French (2008). We ﬁnd very weak evidence of skill
in gross returns, and net-of-fee excess returns are statistically indistinguishable from
their simulated counterparts.
Despite these weak aggregate and average performance statistics, because there is
large cross-sectional variation in performance, it may still be the case that institutional
products that deliver superior performance in one period continue to do so in the future.
Evidence of such persistence could represent a violation of eﬃcient markets, and, for
plan sponsors, represent an important justiﬁcation for selecting investment managers
based solely on performance. We judge persistence in two ways. First, we form deciles
based on benchmark-adjusted returns and estimate alphas over subsequent intervals
using factor models. We calculate alphas over short horizons (one quarter and one year)
to compare them to the retail mutual fund literature, and over long horizons to address
whether plan sponsors can beneﬁt from chasing winners and/or avoiding losers. Second,
we estimate Fama-MacBeth (1973) cross-sectional regressions of risk-adjusted returns on
lagged returns over similar horizons. The latter approach allows us to introduce control
variables (such as assets under management and ﬂows).
For losers, there is evidence of reversals, but it is modest at best. This may come from
look-ahead issues in some tests (see Carpenter and Lynch (1999) and Horst, Nijman,
and Verbeek (2001)), and/or from economies of scale for smaller-sized portfolios. For
winners, using the three-factor model, the alpha of the extreme winner decile one year
(one quarter) after ranking is 0.96% (1.52%) with a t-statistic of 2.79 (3.55). However,
after controlling for the mechanical eﬀect of momentum (that winner products have win-
ner stocks, which are likely to be in the portfolio during the post-ranking period), the
one-year (one-quarter) alpha shrinks to 0% (0.18%) per quarter and is statistically indis-
tinguishable from zero. Persistence regressions show similar results over these horizons.
Thus, at best (using three-factor models) there is modest evidence of persistence over one
year; at worst (using four-factor models) there is no persistence in returns. Over evalua-
tion horizons longer than one year, no measurement technique shows positive top-decile
Earlier studies that examine performance and persistence in institutional investment
management are hampered either by survivorship bias, a short time series (which limits
the power of time series-based tests), or design. The ﬁrst of these studies, Lakonishok,
Shleifer, and Vishny (1992), examines the performance of 341 investment management
ﬁrms between 1983 and 1989. They ﬁnd that performance is poor on average, and ac-
knowledge that although some evidence of persistence exists, data limitations prevent
a robust conclusion. Coggin, Fabozzi, and Rahman (1993) also ﬁnd that investment
managers have limited skill in selecting stocks. Ferson and Khang (2002) use portfolio
weights to infer persistence, and Tonks (2005) examines the performance of U.K. pension
fund managers between 1983 and 1997. Both ﬁnd some evidence of excess performance
but with small samples. Christopherson, Ferson, and Glassman (1998) also ﬁnd some
evidence of persistence among 185 investment managers between 1979 and 1990, but
their sample also suﬀers from survival bias. Goyal and Wahal (2008), while not directly
interested in persistence, report that plan sponsors hire investment managers after large
postive excess returns, but that post-hiring returns are zero; in contrast, pre-ﬁring re-
turns are not exclusively negative and post-ﬁring returns display modest reversion. It
is tempting to conclude that there is no persistence based on their results but such a
conclusion does not necessarily follow. As they describe in their paper, the hiring and
ﬁring of investment managers is inﬂuenced by factors unrelated to performance. For
example, investment managers may be ﬁred because of personnel turnover at the invest-
ment management ﬁrm or reallocations of investment mandates from one asset class to
another. In addition to agency considerations unrelated to performance, institutional
frictions such as minority ownership of the investment manager, the use of an invest-
ment consultant, etc., can inﬂuence these decisions. This means that post-hiring and
post-ﬁring returns are also aﬀected by selection mechanisms that are uncorrelated with
performance, thereby making it diﬃcult to make precise inferences about persistence in
the universe of investment managers. In contrast, our paper tackles the subject head-on,
with the largest sample to date that is uncontaminated by survivorship bias.
Our results are both of economic and practical signiﬁcance. Clearly, economic inter-
pretation in the context of eﬃcient markets depends on the benchmark one chooses to
consider. An investor happy with the CAPM or three-factor model might reasonably
conclude that institutional investment managers deliver superior returns with some per-
sistence. However, an investor intent on incorporating momentum into the analysis is
unlikely to be so sanguine. Moreover, as we show in the robustness section of the paper,
those partial to conditional methods in the spirit of Ferson and Schadt (1996) and more
recent benchmarking methods that incorporate other passive portfolios (Cremers, Peta-
jisto, and Zitzewitz (2008)) also face mixed evidence. To us, on balance, it is diﬃcult to
make the case for persistence.
What are the practical consequences of this? If one takes the strong view that there
is no persistence, then one logical conclusion might be that plan sponsors should en-
gage in entirely passive asset management. Lakonishok, Shleifer, and Vishny (1992)
point out that if plan sponsors did not chase returns, they would have nothing to do.
Given agency problems, exclusively passive asset management is an unlikely outcome.
Moreover, French (2008) argues that price discovery, necessary to society, requires some
degree of active management. These arguments imply that some degree of active man-
agement must exist and that plan sponsors, in equilibrium, should provide capital to
Our paper proceeds as follows. Section I discusses our data and sample construction.
We discuss the results on performance and persistence in Sections II and III, respectively.
Section IV provides robustness checks, and Section V concludes.
I. Data and Sample Construction
Our data come from Informa Investment Solutions (IIS), a ﬁrm that provides
data, services, and consulting to plan sponsors, investment consultants, and investment
managers. This database contains quarterly returns, benchmarks, and numerous ﬁrm-
and product-level attributes for 6,040 domestic equity products managed by 1,661 insti-
tutional asset management ﬁrms from 1979 to 2008. Although the database goes back
to 1979, it only contains “live” portfolios prior to 1991. In that year, data-gathering
policies were revised such that investment management ﬁrms that exit due to closures,
mergers, and bankruptcies were retained in the database. Thus, data after 1991 are free
from survivorship bias. The average attrition rate over this period is 3.8% per year,
which is higher than the 3% reported by Carhart (1997) for mutual funds. The coverage
of the database is quite comprehensive. We cross-check the number of ﬁrms with two
other similar data providers, Mercer Performance Analytics and eVestment Alliance.
Both the time-series and cross-sectional coverages of the database that we use are better
than the two alternatives.
Several features of the data are important for understanding the results. First,
since investment management ﬁrms typically oﬀer multiple investment approaches, the
database contains returns for each of these approaches. For example, Aronson +John-
son+Ortiz, an investment management ﬁrm with $22 billion in assets, manages 10 port-
folios in a variety of capitalizations and value strategies. The returns in the database
correspond to each of these 10 strategies. Our unit of analysis is each strategy’s return,
which we refer to as a “product.” Second, the database contains “composite” returns
provided by the investment management ﬁrm. The individual returns earned by each
plan-sponsor client (account) may deviate from these composite returns for a variety
of reasons. For example, a public-deﬁned beneﬁt plan may ask an investment man-
agement ﬁrm to eliminate “sin” stocks from its portfolio. Such restrictions may cause
small deviations of earned returns from composite returns. Third, the returns are net of
trading costs, but gross of investment management fees. Fourth, although the data are
self reported, countervailing forces ensure accuracy. The data provider does not allow
investment management ﬁrms to amend historical returns (barring typographical errors)
and requires the reporting of a contiguous return series. Further, the SEC vets these
return data when it performs random audits of investment management ﬁrms. However,
we cannot eliminate the possibility of backﬁll bias. We address this issue in Section IV.
In addition to returns, the database contains descriptive information at both the
product level and the ﬁrm level. Roughly speaking, the descriptive information can
be categorized into data about the trading environment, research, and personnel deci-
sions. For each product, we obtain cross-sectional information on its investment style,
a manager-designated benchmark, and whether it oﬀers a performance fee. We also
extract time-series information on assets under management, annual portfolio turnover,
annual personnel turnover, and a fee schedule.
We impose simple ﬁlters on the data. First, we remove all products that are either
missing style identiﬁcation information or contain non-equity components such as con-
vertible debt. This ﬁlter removes 874 products. Second, we remove all passive products
(358 products) since our interest is in active portfolio management. Finally, we re-
move all products that are also oﬀered as hedge funds (191 products). Our ﬁnal sample
consists of 4,617 products oﬀered by 1,448 ﬁrms.
B. Descriptive Statistics
— Insert Table I about here —
Table I provides basic descriptive statistics of the sample. Panel A shows statis-
tics for each year, and Panel B presents similar information for each investment style.
Style gradations are based on market capitalization (small, mid, large, and all cap) and
investment orientation (growth, core, and value).4
In Panel A, the second column shows the number of active domestic equity institu-
tional products from 1991 to 2008. The number of available products rises monotonically
from 1991 to 2004, and then declines somewhat in the last four years. By the end of
2008, more than 2,500 products are available to plan sponsors. The third column shows
average assets (in $ millions). Asset data are available for approximately 80% of the
total sample. Average assets generally increase over time with the occasional decline in
some years; most noticeable, and not surprising, is the decline in 2008. By the end of
2008, total assets exceed $2.5 trillion (2,572 products multiplied by average assets of $1
billion). The growth in the number of products and average assets mirrors that of the
mutual fund industry, which also grew considerably during this time period (Investment
Company Institute (2008)). Average portfolio turnover (shown in column 4) increases
over time, from 60.7% in 1991 to 75.2% in 2007. The increase in turnover is gradual
except for the occasional spike (e.g., during 2000). Wermers (2000) documents a similar
increase in turnover for mutual funds during his 1975 to 2004 sample period.
The prototypical fee structure in institutional investment management is such that
management fees decline as a step function of the size of the mandate delegated by
the plan sponsor. Although ﬁrms can have diﬀerent breakpoints for their fee schedules,
our data provider collects marginal fee schedules using standardized breakpoints of $10
million, $50 million, and $100 million. The marginal fees for each breakpoint are based
on fee schedules; actual fees are individually negotiated between investment managers
and plan sponsors. Larger plan sponsors typically are able to negotiate fee rebates. Some
investment management ﬁrms oﬀer most-favored-nation clauses, but our database does
not contain this information. To our knowledge, no available database details actual
fee arrangements, so we work with the pro forma fee schedules. The last three columns
show average annual pro forma fees (in percent) assuming investment of $10 million,
$50 million, and $100 million, respectively. Not surprisingly, average fees decline as
investment levels increase. Fees are generally stable over time, varying no more than 2
or 3 basis points over the entire time period.
Panel B shows that all major investment styles are represented in our sample. As of
the end of 2008, the largest number of products (417) reside in large-cap value, whereas
the smallest are in all-cap growth (66). To allow for across-style comparisons without
any time-series variation, we present values of assets, turnover, and fees as of the end
of 2008. Generally, average portfolio sizes are biggest for large-cap products. Turnover
is highest for small-cap products; the average turnover for small-cap growth is 105.8.
Considerable variation also exists in fees across investment styles. Again, small-cap
products have the highest fees, and large-cap products have the lowest fees. Although
not shown in the table, intrastyle variation in fees is extremely small; almost all of the
cross-sectional variation in fees is generated by investment styles.
A. Measurement Approach
Following convention in the mutual fund literature, our primary approach to mea-
suring performance is to estimate factor models using time-series regressions. To gen-
erate aggregate measures of performance, we create equal- and value-weighted portfolio
returns of all products available in that quarter. The weight used for value-weighting
is based on the assets in that product at the end of December of the prior year. With
these returns, we estimate:
rp,t − rf,t = αp + βp,k fk,t + p,t , (1)
where rp is the portfolio return, rf is the risk-free return, fk is the k th factor return,
and αp is the abnormal performance measure of interest. To compute CAPM alphas, we
use the excess market return as the only factor. For Fama-French (1993) alphas, we use
market, size, and book-to-market factors. Since Fama and French (2004) maintain that
momentum remains an embarrasment to the three-factor model, and since it appears
to have become the conventional way to measure performance, we also estimate a four-
factor model. We obtain these four factors from Ken French’s web site.5
We also calculate a variety of performance measures for each product. First, we
estimate alphas using the factor models described above. This is only possible if the
product has a long enough return history to reliably estimate the regression. We require
20 quarterly observations to estimate the alpha for each product. Since this requirement
imposes a selection bias (potentially removing underperforming products), we do not
interpret these results in assessing aggregate performance. Rather, our only purpose is
to gauge cross-sectional variation in performance.
Second, we calculate benchmark-adjusted returns by simply subtracting a benchmark
return from the quarterly raw return,
rxi,t = ri,t − rb,t , (2)
where ri is the return on institutional product i, rb is the benchmark return, and rxi
is the excess return. Such benchmark-adjusted returns are widely used by practitioners
for evaluation purposes.
B. Aggregate Performance
— Insert Table II about here —
Panel A of Table II shows estimates of aggregate measures of performance. In
addition to equal- and value-weighted gross returns, we also present parallel results for
net returns. To compute net returns, we ﬁrst calculate the time-series average pro forma
fee based on a $50m investment in that product. We then subtract one quarter of this
annual fee from the product’s quarterly return.
The equal-weighted CAPM alpha is an impressive 0.57% per quarter with a t-statistic
of 3.17. Since raw returns have signiﬁcant exposure to size and value factors, the equal-
weighted three-factor model alpha is reduced to 0.35% per quarter with a t-statistic of
2.52. Value-weighting the returns further reduces the alpha to -0.01 with a t-statistic of
0.05, suggesting that much of the superior performance comes from small products. Even
using simple benchmark-adjusted returns, value-weighting makes a diﬀerence. Average
equal-weighted benchmark-adjusted returns are 0.49% (with a t-statistic of 3.36), but
value-weighted benchmark-adjusted returns are only 0.16% (with a t-statistic of 1.11).
As with mutual funds, controlling for stock momentum makes a big diﬀerence – the
equal-weighted four-factor alpha shrinks to 0.20% per quarter with a t-statistic of only
1.34, and the value-weighted four-factor alpha increases to 0.05% per quarter with a
t-statistic of 0.40. Finally, as expected, incorporating fees shrinks both three- and four-
factor alphas considerably and eliminates any statistical signiﬁcance. The diﬀerence in
alpha from equal-weighted gross and net returns is approximately 18 basis points per
quarter, or 74 basis points per year. This roughly corresponds to the annual fees reported
in Table I.
There is little evidence that, on aggregate, the products oﬀered by institutional in-
vestment management ﬁrms deliver risk-adjusted excess returns. Of course, it is entirely
possible that some investment managers deliver superior returns. We turn to the distri-
bution of product performance next.
C. Distribution of Performance
Panel B of Table II shows the cross-sectional distribution of performance mea-
sures using gross returns. We report the mean as well as the 5th, 10th, 50th (median),
90th, and 95th percentiles. Before proceeding, we urge caution in interpretation for two
reasons. First, as indicated earlier, we require 20 quarterly observations to estimate a
product’s alpha. This naturally creates an upward bias in our estimates since short-
lived products are more likely to be underperformers. Second, statistical inference is
diﬃcult. The individual alphas are cross-sectionally correlated. In principle, one could
compute the standard error of the mean alpha (the cross-sectional average of the indi-
vidual alphas). However, this would require an estimate of the N × N covariance matrix
of the estimated alphas. Since our sample is large (N = 4, 617), computational limita-
tions preclude this approach. Therefore, we provide the percentiles of the cross-sectional
distribution of individual t-statistics, rather than a single t-statistic for the mean alpha.
The average quarterly benchmark-adjusted return is 0.52% per quarter. If a plan
sponsor evaluates the performance of institutional products using simple style bench-
marks, then it might appear that, on average, institutional investment managers deliver
superior performance. The cross-sectional distribution of alphas shows an interesting
progression between the one-, three-, and four-factor models. For example, the mean al-
pha declines from 0.60% per quarter for the CAPM to 0.34% for the Fama-French (1993)
three-factor model to 0.20% for the four-factor model. Similarly, the mean t-statistics
decline from 0.81 for the CAPM to 0.44 for the three-factor model and eventually to 0.35
for the four-factor model. As with the aggregate results in Panel A, the sophistication
of risk adjustment aﬀects inference.
The tails of the distribution are interesting in their own right. Products that are in
the 5th percentile have a four-factor alpha of -0.96%, and the 5th percentile of t-statistics
is -1.44. However, the distribution is right-skewed. The four-factor alpha for the 95th
percentile is 1.45% per quarter, and the corresponding t-statistic is 2.08. Thus, products
in the top 5th (and perhaps even the top 10th) percentile deliver large returns.6 Are
these tails populated by truly skilled funds or by funds that just happened to get lucky?
We examine this question next.7
D. Skill or Luck
It is possible that some of the estimated alphas are high because of luck. To dis-
entangle luck from true skill, we utilize the approach of Kosowski et al. (2006). Kosowski
et al. bootstrap the returns of products under the null of zero alpha and then base their
inference on the entire cross-section of simulated alphas and their t-statistics. We im-
plement their procedure with the modiﬁcation proposed by Fama and French (2008).8
The reader is referred to these papers for further details on the simulation technique.
— Insert Table III about here —
We use the four-factor model to conduct this experiment. The simulation can be
conducted using alphas or t-statistics. We use both in the interest of completeness but
advocate caution in interpretation of results based on alphas. Alphas are estimated
imprecisely and simulation results based on t-statistics are inherently superior because
they control for the precision of each estimate of alpha. This weighting is all the more
important because our (relatively short) time series relies on quarterly (not monthly)
returns. Therefore, we conduct our inference largely from t-statistic-based simulations.
Table III presents the results. Panel A presents the results for alphas from gross returns,
while Panel B shows the same results for alphas from net returns. In each panel, we
show the percentiles of actual and simulated (averaged across 1,000 simulations) alphas
and their t-statistics. We also show the percentage of simulation draws that produce
an alpha/t-statistic greater than the corresponding actual value. This column can be
interpreted as a p-value of the null that the actual value is equal to zero.
For the vast majority of the percentiles of alphas that we report, the actual alphas
are less than the simulated alphas in more than 5% of the cases. Using t-statistics, which
as described above are preferable, there is only one case (the 60th percentile) in which
the simulated t-statistics are less than the actual t-statistic in less than 5% of the draws.
Using alphas, this is also the case in only one situation (the 99th percentile). Examining
net returns to plan sponsors (Panel B), the distribution of actual alphas or t-statistics
is indistinguishable from their simulated counterparts. Overall, there seems to be very
little evidence of skill.
Persistence in performance is important from an economic and practical per-
spective. From an economic view, if prior-period performance can be used to predict
future returns, this represents a signiﬁcant challenge to market eﬃciency. From a plan
sponsor’s perspective, performance represents an important (but not the only) screening
mechanism. If little or no persistence exists in institutional product returns, then any
attempt to select superior performers is likely futile.
We use two empirical approaches to measure persistence. Our ﬁrst approach follows
the mutual fund literature, with minor adjustments to accommodate certain facets of
institutional investment management. The second approach uses Fama-MacBeth (1973)
style cross-sectional regressions to get at persistence while controlling for other variables.
A. Persistence Across Deciles
We follow Carhart (1997) and form deciles during a ranking period and then ex-
amine returns over a subsequent post-ranking period. However, unlike Carhart (1997),
we form deciles based on benchmark-adjusted returns rather than raw returns for two
reasons. First, plan sponsors frequently focus on benchmark-adjusted returns, at least
in part because expected returns from benchmarks are useful for thinking about broader
asset allocation decisions in the context of contributions and retirement withdrawals.
Second, sorting on raw returns could cause portfolios that follow certain types of invest-
ment styles to systematically fall into winner and loser deciles. For example, small cap
value portfolios may fall into winner deciles in some periods, not because these portfo-
lios delivered abnormal returns, but because this asset class generated large returns over
that period (see Elton et al. (1993)). Using benchmark-adjusted returns to form deciles
circumvents this problem.
Beginning at the end of 1991, we sort portfolios into deciles based on the prior annual
benchmark-adjusted return. We then compute the equal-weighted return for each decile
over the following quarter. As we expand our analysis to examine persistence over longer
horizons, we compute this return over appropriate future intervals (e.g., for the ﬁrst year,
our holding period is from quarter one through quarter four; for the second year, the
holding period is from quarter ﬁve through quarter eight, etc.). We then roll forward,
producing a non-overlapping set of post-ranking quarterly returns. Concatenating the
post-ranking period quarterly returns results in a time series of post-ranking returns for
each portfolio; we generate estimates of abnormal performance from these time series.
Similar to our assessment of average performance in the previous section, we assess
post-ranking abnormal performance by regressing the post-ranking gross returns on K
factors as follows:
rd,t − rf,t = αd + βd,k fk,t + d,t , (3)
where rd is the return for decile d, and fk is the k th factor return. We use factors identical
to those described above in equation (1).
— Insert Table IV about here —
Table IV shows alphas corresponding to CAPM, three-factor, and four-factor models
in Panels A, B, and C, respectively. We report four diﬀerent post-ranking horizons:
one quarter to draw inferences about short-term persistence and the ﬁrst, second, and
third year after ranking. Note that the second- and third-year alphas are not multiyear
alphas; for example, the second-year alpha pertains to the performance of the decile in
the second year after ranking, not the performance over the ﬁrst and second year. This
follows convention and avoids overlapping observations in the statistical tests.9
We estimate alphas for each decile and horizon, but this generates a large number
of statistics to report. To avoid overwhelming the reader with a barrage of numbers, we
only report alphas for some of the deciles. We report select loser deciles (1, 2, and 3)
for comparison with the mutual fund literature. We also report alphas for three winner
deciles (8, 9, and 10) and one intermediate decile (5). The variation in benchmark-
adjusted returns used to create deciles is large (ranging from -13.6% in decile 1 to 22.2%
for decile 10).
We start with a discussion of losers. There is a modest reversal in fortunes for
products in the extreme loser deciles, relative to the CAPM, and to some degree, the
three-factor model. For instance, the extreme loser decile has an alpha of 0.52% (0.35%)
per quarter in the second (third) year after ranking, with a t-statistic of 2.82 (1.65). Con-
trolling for momentum, these estimates and their statistical signiﬁcance drop sharply:
the corresponding four-factor alpha is 0.37% (0.07%) with a t-statistic of 1.85 (0.30).
Modest reversion could arise from two sources: economies of scale and look-ahead is-
sues. With regard to the ﬁrst, if a product performs poorly and loses assets, its future
performance may improve simply because it is better able to manage a smaller asset
base. For example, this can arise from proportionately lower trading costs. However,
measuring this requires detailed information on the cost function of each product. Such
data are not available to us, but broad evidence for mutual funds reported in Chen
et al. (2004) is consistent with such an eﬀect; in the Internet Appendix (available at
http://www.afajof.org/supplements.asp) we report regressions that are similar to those
of Chen et al. (2004) for our sample. We can, however, get a handle on look-ahead issues.
As Carpenter and Lynch (1999) and Horst, Nijman, and Verbeek (2001) point out, look-
ahead issues in nonsurvivorship-biased samples could generate spurious reversals. Since
poorly performing products are more likely to disappear, the expected attrition rate in
decile 1 is higher than decile 10. In our data, by the third year after decile formation,
decile 1 has lost 19% of its constituents, whereas decile 10 has lost only 8%. The average
benchmark-adjusted return in the last year before disappearing for portfolios in decile 1
is -5.4% and 3.5% for decile 10.10 We gauge the impact of diﬀerential attrition on loser
deciles with a simple exercise. Speciﬁcally, we assume the population alpha is normally
distributed with mean zero and standard deviation σα . We then calculate the mean of
a truncated normal distribution for various values of σα and degrees of truncation. The
Appendix at the end of this article reports the results of this exercise. Under a true null
of zero alpha, and assuming a σα of 0.8% (inferred from the cross-sectional distribution
of alphas in Table II), 20% truncation in population imparts an upward bias in alpha
of 0.28%. This adjustment would reduce the one-, three-, and four-factor alphas of the
loser decile in the third year after portfolio formation to 0.28, 0.07, and -0.21 percent,
respectively (versus 0.52, 0.35, and 0.07 reported in Table IV), all of which would be
With respect to winners, over a one-quarter horizon, some evidence of persistence
exists in winner deciles, at least based on the one- and three-factor models. For exam-
ple, the alpha for decile 10 under the CAPM (three-factor) model is 1.17% (1.52%) per
quarter with a t-statistic of 2.26 (3.55). The spread between the extreme winner and
loser decile (10-1) is also high, and in the case of the three-factor model, statistically
signiﬁcant. However, as Carhart (1997) shows and Fama and French (2008) conﬁrm, mo-
mentum plays an important role in persistence. Winner deciles are more likely to have
winner stocks, and given individual stock momentum, this mechanically generates per-
sistence among winners. Consistent with this, persistence in the extreme winner decile
falls dramatically to a statistically insigniﬁcant 0.18% after controlling for momentum.
By comparison, Bollen and Busse (2005) report a one-quarter post-ranking four-factor
alpha of 0.39% for the winner decile of mutual funds.
Although short-term (one-quarter) persistence is interesting from an economic per-
spective, plan sponsors do not deploy capital for such short horizons. The transaction
costs (known as transition costs) from exiting a product after one quarter and entering a
new one are large, in addition to adverse reputation eﬀects from rapidly trading in and
out of institutional products. Persistence over long horizons is also more important from
a practical and economic perspective. Long-horizon persistence represents a violation of
market eﬃciency and a potentially value-increasing opportunity for plan sponsors.
One year after decile formation, the three-factor alpha for the extreme winner decile is
high (0.96%) and statistically signiﬁcant (t-statistic of 2.79). But once again, controlling
for momentum reduces the alpha to 0.00% and eliminates the statistical signiﬁcance. In
the second and third year after decile formation, no evidence of persistence exists using
either the three- or four-factor model in any of the winner deciles. Thus, over the long
horizons over which plan sponsors typically conduct their investments, minimal evidence
of persistence exists.
B. Regression-based Evidence
Our second approach to measuring persistence involves estimating Fama-MacBeth
(1973) regressions of future returns on lagged returns at various horizons and aggregating
coeﬃcients over time:
rp,t+k1 :t+k2 − rb,t+k1 :t+k2
= γ0,t + γ1,t (rp,t−k:t − rb,t−k:t ) + γ2,t Zp,t + ep,t , or (4)
(rp,t+k1 :t+k2 − rf,t+k1 :t+k2 ) − βp,k fk,t+k1 :t+k2
= γ0,t + γ1,t (rp,t−k:t − rb,t−k:t ) + γ2,t Zp,t + ep,t , (5)
where t + k1 to t + k2 is the horizon over which we measure future returns, t − k to t is the
horizon over which we measure the lagged returns, and Zp represents control variables.
The ﬁrst speciﬁcation considers persistence in benchmark-adjusted returns, while the
second speciﬁcation considers the same in risk-adjusted returns. Following Brennan,
Chordia, and Subrahmanyam (1998), we estimate the betas in the second speciﬁcation
with a ﬁrst-pass time-series regression using the whole sample. In addition, because
the betas in the second speciﬁcation are estimated, we adjust the γ coeﬃcients using
the Shanken (1992) errors-in-variables correction. Finally, we adjust the Fama-MacBeth
(1973) standard errors for autocorrelation because of the overlap in the dependent and/or
This approach has three advantages. First, it oﬀers more ﬂexibility in exploring
horizons over which persistence may exist. Second, it allows us to examine persis-
tence in benchmark-adjusted returns. To the extent that practitioners use benchmark
adjustments to help them decide where to allocate capital, examining persistence in
benchmark-adjusted returns may be of interest. Third, it allows us to directly control
for product-level attributes (such as total assets) that may aﬀect future returns and
persistence. This is particularly useful in the presence of diseconomies of scale.
— Insert Table V about here —
Table V presents the results of these regressions. We use four dependent variables
corresponding to benchmark-adjusted returns, and one-, three-, and four-factor model
risk-adjusted returns described in equations (4) and (5). As in the decile-based tests,
the horizons of the dependent variables are the ﬁrst quarter and the ﬁrst, second, and
third year ahead.
In Panel A, the explanatory variables are the previous quarter’s return, the prior
year’s return, the prior two-year holding period return, or the prior three-year holding
period return. Panel B augments these regressions with one-year lagged assets under
management for the product as well as the ﬁrm, and with prior-year ﬂows.
Consistent with the results reported in Table IV, modest evidence of persistence
exists at the one-quarter and one-year horizon with one- and three-factor model adjusted
returns; the coeﬃcients on lagged returns over these horizons are positive and generally
statistically signiﬁcant. For instance, when we regress the one-year-ahead three-factor
model return against lagged one-year returns, the average coeﬃcient is 0.090 with a
t-statistic of 2.07. The addition of control variables in Panel B, however, reduces this
coeﬃcient to 0.072 and the t-statistic to 1.53. Even without these control variables, the
addition of momentum drops the corresponding coeﬃcient to 0.071 and the t-statistic
to 1.41. Over horizons longer than a year for either the dependent variable or the
independent variables, there is no statistical evidence of persistence.
It is common practice among plan sponsors and investment consultants to focus
on performance and persistence using benchmark-adjusted returns. The results using
benchmark-adjusted returns are, in fact, diﬀerent from those using factor models. Pan-
els A and B show considerable persistence in one-year forward benchmark-adjusted re-
turns. This predictability is evident with one- and two-year holding period returns (and
marginally even three-year holding period returns). Thus, using benchmark-adjusted
returns, a plan sponsor could reasonably argue that using prior performance to pick
institutional products and accordingly allocate capital is appropriate.
Overall, judging a variety of results across diﬀerent factor models and estimation
techniques, we believe that the results show little to no evidence on persistence in insti-
A. Data Veracity
A natural concern in the measurement of performance and persistence is the degree
to which the data accurately represent the population. A common concern is whether
the data adequately represent poorly performing products. This issue could arise from
backﬁll bias (Liang (2000)), the use of incubation techniques by investment management
ﬁrms (Evans (2007)), or similar selective reporting. These problems likely can never be
perfectly detected or eliminated. Consequently, we follow two approaches to determine
their potential impact on our results. First, we follow Jagannathan, Malakhov, and
Novikov (2006) and eliminate the ﬁrst three years of returns for each product and re-run
our main tests. Our regressions are inevitably noisier, but our basic results are unchanged
and reported in the Internet Appendix.11 Second, we return to the Appendix at the end
of this article to determine the impact of truncation. Recall that the largest three-factor
(four-factor) alphas in our sample for equal-weighted gross returns are 0.35% (0.20%)
per quarter. If we assume a σα of 0.80% based on analysis reported in Panel B of Table
II, then the Appendix shows that over 22% (18%) of the left tail of the distribution
would have to be truncated to produce an alpha of 0.35% (0.20%). To us, such a high
degree of truncation seems implausible.
B. Performance Measurement
In addition to data issues, performance measurement is subject to a variety of con-
cerns about choice of asset pricing models, model speciﬁcation, and estimation issues.
Despite numerous rejections of the standard CAPM, we continue to report CAPM alphas
because it at least represents an equilibrium model of expected returns. We also report
Fama-French (1993) alphas because of their ability to capture cross-sectional variation in
returns and Carhart’s (1997) four-factor alphas because of their widespread acceptance
in this literature. However, at least two other approaches are important to consider:
using conditioning information and expanded factor models.
Christopherson, Ferson, and Glassman (1998) argue that unconditional performance
measures are inappropriate for two reasons (see also Ferson and Schadt (1996)). First,
they note that sophisticated plan sponsors presumably condition their expectations
based on the state of the economy. Second, to the extent that plan sponsors employ
dynamic trading strategies that react to changes in market conditions, unconditional per-
formance indicators may be biased. They advocate conditional performance measures
and show that such measures can improve inference. We follow their prescription and
estimate conditional models in addition to the unconditional models described earlier.
We estimate the conditional models as
rp,t = αC
p + 0
βp,k + l
βp,k Zl,t−1 fk,t + p,t , (6)
where the Z’s are L conditioning variables.
We use four conditioning variables in our analysis. We obtain the three-month T-bill
rate from the economic research database at the Federal Reserve Bank in St. Louis.
We compute the default yield spread as the diﬀerence between BAA- and AAA-rated
corporate bonds using the same database. We obtain the dividend-price ratio, computed
as the logarithm of the 12-month sum of dividends on the S&P 500 index divided by
the logarithm of the index level from Standard & Poor’s. Finally, we compute the term
yield spread as the diﬀerence between the long-term yield on government bonds and the
T-bill yield using data from Ibbotson Associates.
— Insert Table VI about here —
Table VI reports the results of these regressions. Panel A (B) shows conditional
alphas for the persistence tests based on three- (four-) factor models. We urge some
caution in interpretation because of potential overparameterization. Four factors and
four conditioning variables result in 21 independent variables, which obviously reduces
the degrees of freedom in our regressions. At the same time, we ﬁnd that the aver-
age R improvement from conditional models over their unconditional counterparts is
very modest. Despite these issues, the results largely mirror the unconditional estimates
reported earlier; there is evidence of persistence in three-factor models one year after
ranking but it shrinks appreciably in both magnitude and statistical signiﬁcance once
we control for momentum. For instance, the one-year three-factor post-ranking alpha
of the extreme winner decile is 1.24% per quarter with a t-statistic of 3.45, and the
extreme winner-minus-loser spread is 0.95 (t-statistic=2.20). But, controlling for the
mechanical eﬀects of momentum, the four-factor alpha for the extreme winner decile
drops to 0.43% with a t-statistic of 1.77 (and the winner-minus-loser spread declines to
0.23 with t-statistic of 0.65). Similar to the conditional models, there is no persistence
beyond the ﬁrst year.
We also consider factor models augmented by index portfolios. Cremers, Petajisto,
and Zitzewitz (2008) report that, when evaluated using three- and four-factor models,
passive portfolios such as the S&P 500 and Russell 2000 produce economically meaningful
alphas. They argue that this is because these factor models place disproportionate weight
on small and/or value stocks; one of their proposed “solutions” is to estimate a seven-
factor model that is based on index returns. This model, labeled “IDX7” in their paper,
includes the following factors: the S&P 500, the diﬀerence between the Russell Midcap
and the S&P 500, the diﬀerence between the Russell 2000 and the Russell Midcap, the
diﬀerence between the S&P 500 Value and S&P 500 Growth, the diﬀerence between the
Russell Midcap Value and Russell Midcap Growth, the diﬀerence between the Russell
2000 Value and Russell 2000 Growth, as well as the momentum factor. We repeat
our analysis with this seven-factor model and report the results in Panel C. Here the
results are weaker than the conditional models. The one-year post-ranking alpha of the
extreme winner decile is 0.37% with a t-statistic of 1.48. This is higher than the four-
factor unconditional model results but lower than the conditional model results reported
in Panels A and B.
Thus, while there are inevitably diﬀerences in parameter estimates between the un-
conditional and conditional models, and between the four- and seven-factor models, the
basic ﬂavor of the results is similar.
C. Statistical Signiﬁcance
Finally, we gauge statistical signiﬁcance in the paper with regular t-statistics. Kosowski
et al. (2006) recommend a bootstrap procedure to alleviate problems of small sample
and skewness in the returns data. We follow their approach to calculate bootstrapped
standard errors. Speciﬁcally, we assume that equations (1) and (6) with zero alpha de-
scribe the data-generating process. We generate each draw by choosing at random (with
replacement) the saved residuals. We then compute alphas and their t-statistics from
the bootstrapped sample. We repeat this procedure 1,000 times to obtain the empirical
distribution of t-statistics. The statistical signiﬁcance using critical values from this
bootstrapped distribution is very similar to that reported in the rest of the paper.
In this paper, we examine the performance and persistence in performance of
4,617 active domestic equity institutional products managed by 1,448 investment man-
agement ﬁrms between 1991 and 2008. Plan sponsors use these products as investment
vehicles to delegate portfolio management in investment styles across all size and value-
Before fees, we ﬁnd little evidence of superior performance, either in aggregate or on
average. However, even if no evidence of aggregate superior performance exists, it could
be the case that some investment managers deliver superior returns over long periods.
In addition to its own economic interest, such persistence is of enormous practical value,
since plan sponsors routinely use performance in allocating capital to these ﬁrms. Our
estimates of persistence are sensitive to the choice of models. Three-factor models show
modest evidence of persistence and would cause an investor to use performance as a
screening device. In contrast, four-factor models that incorporate the mechanical eﬀects
of stock momentum show little to no persistence. Conditional four-factor models and
seven-factor models paint a similar picture.
Appendix: Mean of a Truncated Normal Distribution of Alphas
We assume the population α is distributed normally with mean zero and standard
deviation σα . The table reports the mean of a truncated distribution where the left tail
of the distribution is truncated (unobserved).
Fraction of population that is left-truncated
σα 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
0.1% 0 0.01 0.02 0.03 0.03 0.04 0.05 0.06 0.06 0.07 0.08
0.2% 0 0.02 0.04 0.05 0.07 0.08 0.10 0.11 0.13 0.14 0.16
0.3% 0 0.03 0.06 0.08 0.10 0.13 0.15 0.17 0.19 0.22 0.24
0.4% 0 0.04 0.08 0.11 0.14 0.17 0.20 0.23 0.26 0.29 0.32
0.5% 0 0.05 0.10 0.14 0.17 0.21 0.25 0.28 0.32 0.36 0.40
0.6% 0 0.07 0.12 0.16 0.21 0.25 0.30 0.34 0.39 0.43 0.48
0.7% 0 0.08 0.14 0.19 0.24 0.30 0.35 0.40 0.45 0.50 0.56
0.8% 0 0.09 0.16 0.22 0.28 0.34 0.40 0.46 0.52 0.58 0.64
0.9% 0 0.10 0.18 0.25 0.31 0.38 0.45 0.51 0.58 0.65 0.72
1.0% 0 0.11 0.19 0.27 0.35 0.42 0.50 0.57 0.64 0.72 0.80
1.1% 0 0.12 0.21 0.30 0.38 0.47 0.55 0.63 0.71 0.79 0.88
1.2% 0 0.13 0.23 0.33 0.42 0.51 0.60 0.68 0.77 0.86 0.96
1.3% 0 0.14 0.25 0.36 0.45 0.55 0.65 0.74 0.84 0.94 1.04
1.4% 0 0.15 0.27 0.38 0.49 0.59 0.70 0.80 0.90 1.01 1.12
1.5% 0 0.16 0.29 0.41 0.52 0.64 0.75 0.85 0.97 1.08 1.20
1.6% 0 0.17 0.31 0.44 0.56 0.68 0.79 0.91 1.03 1.15 1.28
1.7% 0 0.18 0.33 0.47 0.59 0.72 0.84 0.97 1.09 1.22 1.36
1.8% 0 0.20 0.35 0.49 0.63 0.76 0.89 1.03 1.16 1.30 1.44
1.9% 0 0.21 0.37 0.52 0.66 0.81 0.94 1.08 1.22 1.37 1.52
2.0% 0 0.22 0.39 0.55 0.70 0.85 0.99 1.14 1.29 1.44 1.60
See, for example, Grinblatt and Titman (1992), Hendricks, Patel, and Zeckhauser
(1993), Brown and Goetzmann (1995), Elton, Gruber, and Blake (1996), and Wermers
By way of comparison, Gruber (1996) estimates a CAPM alpha of -13 basis points
per month after expenses for mutual funds. Wermers (2000) estimates that mutual funds
outperform the S&P 500 by an average of 2.3% per year before expenses and trading
costs and underperform the S&P 500 by an average of 50 basis points per year net of
expenses and trading costs.
For mutual funds, Bollen and Busse (2005) report a four-factor alpha of 0.39% for
the top decile in the post-ranking quarter. Kosowski et al. (2006) report a statistically
signiﬁcant monthly alpha of 0.14% in the extreme winner decile for the ﬁrst year.
Size breakpoints in this database are as follows: small caps are those less than $2
billion, mid-caps are between $2 and $7 billion, and large caps are larger than $7 billion.
Ken French’s momentum factor is slightly diﬀerent from the one employed by Carhart
(1997). Carhart calculates his momentum factor as the equal-weighted average of ﬁrms
with the highest 30% 11-month returns (lagged one month) minus the equal-weighted
average of ﬁrms with the lowest 30% 11-month returns. French’s momentum factor fol-
lows the construction of the book-to-market factor (HML). It uses six portfolios, splitting
ﬁrms on the 50th percentile of NYSE market capitalization and on 30th and 70th per-
centiles of the two- through 12-month prior returns for NYSE stocks. Portfolios are
value-weighted, use NYSE breakpoints, and are rebalanced monthly.
We remind the reader that the distribution is shifted to the right because of the
requirement that at least 20 return observations be available to estimate alphas. This
reduces the number of products for this exercise from 4,617 to 3,842. Not surprisingly,
the average benchmark-adjusted return for the eliminated products is 0.17% lower than
that for the remaining products, generating the right shift.
We also estimate regressions of four-factor alphas on a variety of variables that proxy
for research activities, trading, and the composition of human capital. These regressions
are interesting but noisy so we report them in the Internet Appendix rather than in
the body of the paper. The dependent variable (four-factor alpha) is measured over the
entire return history of the product and the independent variables are measured over
the entire time series or at the end of the time series. The two most interesting results
from these regressions is that (a) ﬁrms that use sell-side Wall Street research have lower
returns, and (b) ﬁrms that employ Ph.D.s have higher returns. We stress that these
are simply correlations; these results imply no causality. For instance, we can only infer
that better performing ﬁrms have more Ph.D.s. We cannot infer that having Ph.D.s
improved these ﬁrms’ returns, or that higher returns allowed them to hire Ph.D.s.
Kosowski et al. (2006) present their main results when they bootstrap the residuals
for each product independently. Fama and French (2008) sample the product and factor
returns jointly to better account for common variation in product returns not accounted
for by factors, and correlated movement in the volatilities of factor returns and residuals.
For readers interested in multiyear (two- and three-year) alphas, we provide these
in the Internet Appendix.
We also compare these attrition rates and last-year excess returns to those in mutual
fund portfolios and ﬁnd that both are higher for mutual funds than for insitutional funds.
For instance, the three-year attrition rate in domestic equity mutual funds is 25% (versus
18% for our sample), and the last-year excess return is -22% (versus -6% for our sample).
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The table presents descriptive statistics on the sample of institutional investment products.
Asset size is in millions of dollars, turnover is in percent per year, and fees are in percent per
year. The descriptives in Panel B are for the year 2008 only.
Number Average Average Fees
products asset size turnover $10M $50M $100M
Panel A: Descriptives Statistics by Year
1991 1,201 621 60.7 0.79 0.65 0.61
1992 1,357 604 59.5 0.78 0.63 0.58
1993 1,572 628 62.0 0.79 0.65 0.60
1994 1,770 628 61.7 0.77 0.63 0.57
1995 1,953 802 65.5 0.78 0.63 0.58
1996 2,154 897 66.4 0.78 0.68 0.58
1997 2,309 1,068 68.9 0.79 0.64 0.58
1998 2,476 1,150 73.7 0.78 0.64 0.59
1999 2,655 1,254 76.0 0.78 0.65 0.59
2000 2,841 1,125 80.8 0.78 0.65 0.60
2001 3,001 990 78.2 0.79 0.66 0.60
2002 3,065 780 74.0 0.79 0.67 0.61
2003 3,137 1,050 73.6 0.80 0.68 0.62
2004 3,156 1,180 71.7 0.80 0.68 0.62
2005 3,080 1,319 70.4 0.81 0.69 0.63
2006 2,982 1,470 71.6 0.81 0.69 0.63
2007 2,877 1,395 73.3 0.81 0.69 0.63
2008 2,572 1,009 75.2 0.81 0.69 0.64
Panel B: Descriptives Statistics by Style
Small Cap Growth 231 430 105.8 0.94 0.88 0.82
Small Cap Core 161 383 87.4 0.88 0.78 0.71
Small Cap Value 270 591 70.1 0.93 0.86 0.80
Mid Cap Growth 191 595 104.4 0.82 0.72 0.66
Mid Cap Core 89 269 87.3 0.78 0.67 0.60
Mid Cap Value 187 883 67.1 0.84 0.72 0.66
Large Cap Growth 369 1,281 74.7 0.76 0.62 0.57
Large Cap Core 327 1,091 61.1 0.69 0.56 0.50
Large Cap Value 417 2,115 59.6 0.71 0.58 0.51
All Cap Growth 66 763 94.1 0.85 0.75 0.71
All Cap Core 115 1,051 72.9 0.77 0.60 0.54
All Cap Value 149 694 49.5 0.80 0.68 0.63
Distribution of Performance
The CAPM one-factor model uses the market factor. The three factors in the three-factor
model are the Fama-French factors (market, size and book-to-market). The four factors in the
four-factor model are the Fama-French factors augmented with a momentum factor. We choose
the benchmarks based on the investment style of the product to adjust raw returns. Panel A
reports performance measures for portfolios along with their t-statistics in parentheses. We
form portfolios from individual products. Portfolios are both equal- and value-weighted (we
value weight based on asset size from December of the prior year). Returns are either gross or
net of fees (for a mandate of $50 million). Panel B reports the percentiles for performance mea-
sures for gross returns of 3,842 individual products that have at least 20 quarters of available
data. All numbers are in percent per quarter. The sample period is 1991 to 2008.
Benchmark-adjusted Factor model alphas
returns 1-factor 3-factor 4-factor
Panel A: Portfolio Performance
EW Gross 0.49 0.57 0.35 0.20
(3.36) (3.17) (2.52) (1.34)
VW Gross 0.16 0.13 -0.01 0.05
(1.11) (1.06) (-0.05) (0.40)
EW Net — 0.40 0.16 0.01
(2.11) (1.17) (0.05)
VW Net — -0.02 -0.16 -0.10
(-0.15) (-1.39) (-0.79)
Panel B: Individual Product Performance of Gross Returns
5 pcnt -0.67 -0.71 -0.84 -0.96
10th pcnt -0.39 -0.42 -0.60 -0.61
Mean 0.52 0.60 0.34 0.20
Median 0.43 0.51 0.21 0.18
90th pcnt 1.56 1.71 1.38 1.03
95th pcnt 1.99 2.20 1.94 1.45
5th pcnt -1.13 -1.18 -1.63 -1.44
10 pcnt -0.7 -0.68 -1.13 -1.04
Mean 0.77 0.81 0.44 0.33
Median 0.81 0.88 0.45 0.35
90th pcnt 2.16 2.22 2.02 1.69
95th pcnt 2.58 2.66 2.48 2.08
Luck Versus Skill in Performance
Performance is measured using four-factor alphas, similar to that in Table II. The table shows
percentiles of actual and (average) simulated alphas and their t-statistics. The details of the
simulation are described in the text. We also show the percentage of simulation draws that
produce an alpha/t-statistic greater than the corresponding actual value. Alphas are in percent
per quarter. The sample period is 1991 to 2008.
Pct Actual Sim %(Sim>Actual) Actual Sim %(Sim>Actual)
Panel A: Gross Returns (Number of products = 3,842)
1 -1.65 -2.01 18.20 -2.36 -2.74 22.20
2 -1.34 -1.58 23.40 -1.96 -2.34 19.70
3 -1.19 -1.37 27.80 -1.73 -2.11 16.80
4 -1.06 -1.22 27.70 -1.55 -1.95 14.60
5 -0.96 -1.11 26.50 -1.44 -1.82 14.60
10 -0.61 -0.80 15.70 -1.04 -1.40 13.00
20 -0.32 -0.49 14.10 -0.58 -0.92 11.30
30 -0.13 -0.30 9.20 -0.25 -0.59 8.80
40 0.02 -0.15 7.40 0.05 -0.31 7.20
50 0.18 -0.02 5.40 0.35 -0.05 5.50
60 0.32 0.10 5.80 0.63 0.20 4.80
70 0.46 0.25 7.60 0.90 0.48 6.30
80 0.66 0.43 9.10 1.23 0.80 7.10
90 1.03 0.74 8.50 1.69 1.27 8.90
95 1.45 1.06 7.50 2.08 1.67 10.80
96 1.61 1.16 6.70 2.21 1.79 11.10
97 1.79 1.31 7.00 2.39 1.95 11.90
98 2.06 1.52 6.80 2.58 2.17 15.50
99 2.74 1.94 4.10 2.91 2.55 20.80
Panel B: Net Returns (Number of products = 2,987)
1 -1.96 -1.97 53.60 -2.68 -2.67 52.00
2 -1.58 -1.57 55.30 -2.33 -2.30 55.00
3 -1.44 -1.35 64.70 -2.08 -2.08 51.80
4 -1.32 -1.21 68.90 -1.95 -1.92 54.80
5 -1.19 -1.10 65.80 -1.80 -1.80 52.00
10 -0.79 -0.79 53.00 -1.40 -1.38 53.20
20 -0.51 -0.48 56.40 -0.92 -0.91 52.00
30 -0.32 -0.30 56.20 -0.58 -0.58 49.70
40 -0.15 -0.15 48.40 -0.28 -0.31 46.40
50 0.00 -0.02 40.60 0.00 -0.05 40.60
60 0.15 0.10 34.10 0.29 0.20 35.90
70 0.29 0.24 34.60 0.57 0.47 33.00
80 0.49 0.43 29.70 0.92 0.79 29.50
90 0.86 0.73 22.30 1.36 1.25 33.10
95 1.25 1.04 19.30 1.77 1.65 33.70
96 1.41 1.15 17.30 1.92 1.77 32.20
97 1.54 1.29 19.00 2.05 1.92 34.10
98 1.80 1.50 17.70 36 2.23 2.13 38.80
99 2.47 1.89 9.70 2.61 2.49 38.10
Performance Persistence Across Deciles
We sort products in deciles according to the benchmark-adjusted return during the ranking
period of one year. We hold the decile portfolios for post-ranking periods ranging from one
quarter to three years. We rebalance the portfolios at the end of every quarter when the
holding period is one quarter and at the end of every year otherwise. Factor models are the
same as those in Table II. All alphas are in percent per quarter, and t-statistics are reported
in parentheses next to alphas. Decile 1 contains the worst-performing products, and decile 10
contains the best-performing products. The sample period is 1991 to 2008.
Decile 1st quarter 1st year 2nd year 3rd year
Panel A: 1-factor Alphas
1 0.30(0.91) 0.44(1.70) 0.70(3.07) 0.52(2.07)
2 0.30(1.14) 0.40(1.56) 0.59(2.73) 0.52(2.55)
3 0.27(1.21) 0.34(1.67) 0.41(2.32) 0.52(2.81)
5 0.35(2.02) 0.38(2.24) 0.42(2.58) 0.32(1.84)
8 0.52(2.58) 0.49(2.45) 0.46(2.22) 0.33(1.49)
9 0.63(2.49) 0.63(2.76) 0.34(1.41) 0.31(1.19)
10 1.17(2.26) 0.78(1.79) 0.24(0.63) 0.28(0.76)
10-1 0.87(1.30) 0.34(0.71) -0.45(-1.22) -0.24(-0.72)
Panel B: 3-factor Alphas
1 -0.18(-0.66) 0.08(0.37) 0.52(2.82) 0.35(1.65)
2 -0.10(-0.46) -0.01(-0.06) 0.29(1.65) 0.28(1.75)
3 -0.05(-0.28) 0.05(0.29) 0.15(1.11) 0.29(2.06)
5 0.07(0.49) 0.10(0.76) 0.19(1.43) 0.07(0.63)
8 0.26(1.65) 0.23(1.48) 0.22(1.37) 0.09(0.56)
9 0.52(2.62) 0.49(2.80) 0.11(0.62) 0.09(0.41)
10 1.52(3.55) 0.96(2.79) 0.17(0.57) 0.20(0.61)
10-1 1.70(2.88) 0.88(2.09) -0.35(-0.97) -0.16(-0.47)
Panel C: 4-factor Alphas
1 0.32(1.20) 0.30(1.36) 0.37(1.85) 0.07(0.30)
2 0.27(1.19) 0.16(0.72) 0.20(1.04) 0.10(0.58)
3 0.16(0.77) 0.19(0.96) 0.12(0.74) 0.13(0.88)
5 0.07(0.47) 0.14(0.90) 0.15(1.02) 0.06(0.43)
8 0.06(0.35) 0.07(0.40) 0.16(0.91) 0.06(0.34)
9 -0.06(-0.42) 0.04(0.30) 0.09(0.45) 0.01(0.05)
10 0.18(0.66) 0.00(0.00) -0.04(-0.13) -0.09(-0.25)
10-1 -0.15(-0.39) -0.30(-0.95) -0.41(-1.03) -0.15(-0.41)
We estimate the following Fama-MacBeth (1973) cross-sectional regression:
rp,t+k1 :t+k2 − rb,t+k1 :t+k2
= γ0,t + γ1,t (rp,t−k:t − rb,t−k:t) + γ2,t Zp,t + ep,t , or
(rp,t+k1 :t+k2 − rf,t+k1 :t+k2 ) − βp,k fk,t+k1 :t+k2
= γ0,t + γ1,t (rp,t−k:t − rb,t−k:t) + γ2,t Zp,t + ep,t ,
where t + k1 to t + k2 is the horizon over which we measure future returns, t − k to t is the
horizon over which we measure the lagged returns, and Zp represents control variables. The
ﬁrst speciﬁcation uses benchmark-adjusted returns, while the second uses risk-adjusted returns
(we estimate the betas in the second speciﬁcation using a ﬁrst-pass time-series regression using
the whole sample). The controls included in Panel B are lagged asset size at the product- and
ﬁrm-level and lagged cashﬂows. We estimate the regressions each quarter when the dependent
variable is a quarterly return and each year otherwise. The table reports the time-series aver-
age of the γ1 coeﬃcient along with t-statistics (corrected for autocorrelation) in parentheses.
In addition, because the betas in the second speciﬁcation are estimated, we adjust the γ coef-
ﬁcients using the Shanken (1992) errors-in-variables correction. The sample period is 1991 to
Horizon of returns Future return adjustment
Future Lagged Benchmark Factor model
t+k1 :t+k2 t−k:t 1-factor 3-factor 4-factor
Panel A: No Additional Controls
1st quarter 1-quarter 0.077(1.90) 0.031(0.99) 0.047(2.06) 0.040(2.49)
1-year 0.046(3.51) 0.015(1.18) 0.029(3.38) 0.025(3.99)
2-years 0.025(3.24) 0.010(1.09) 0.016(2.76) 0.010(2.36)
3-years 0.015(2.78) 0.007(1.13) 0.009(2.22) 0.003(0.93)
1st year 1-quarter 0.441(3.51) 0.149(1.64) 0.231(2.24) 0.183(2.01)
1-year 0.147(2.40) 0.064(1.49) 0.090(2.07) 0.071(1.41)
2-years 0.082(3.25) 0.027(0.64) 0.044(1.66) 0.015(0.53)
3-years 0.030(1.50) 0.011(0.36) 0.018(1.02) -0.004(-0.19)
2nd year 1-quarter -0.170(-0.94) -0.182(-1.72) -0.133(-1.71) -0.078(-0.80)
1-year 0.071(1.13) -0.016(-0.17) 0.021(0.38) -0.019(-0.28)
2-years 0.012(0.43) -0.011(-0.22) 0.001(0.05) -0.028(-0.73)
3-years 0.024(1.20) -0.014(-0.37) 0.001(0.06) -0.017(-0.56)
3rd year 1-quarter -0.119(-1.10) -0.184(-1.06) -0.166(-1.28) -0.138(-0.88)
1-year 0.002(0.04) -0.003(-0.06) -0.019(-0.65) -0.045(-1.27)
2-years 0.046(1.20) -0.011(-0.19) 0.002(0.09) -0.018(-0.41)
3-years 0.009(0.39) -0.002(-0.07) 0.009(0.47) -0.010(-0.39)